Understanding and Interpreting Scatter Plots

Aug 14, 2024

Lecture Notes: Interpreting Scatter Plots

Introduction to Scatter Plots

  • A scatter plot is a graph used to display values for typically two variables for a set of data.
  • Points on the scatter plot represent individual data points.

Types of Associations in Scatter Plots

No Association

  • Definition: No discernible pattern between the x-axis and y-axis values.
    • Example: Height vs. Maths marks; no correlation between a person's height and their maths ability.

Positive Association

  • Definition: As values on the x-axis increase, values on the y-axis also increase.
    • Example: Temperature vs. Ice cream sales; higher temperatures lead to increased ice cream sales.

Negative Association

  • Definition: As values on the x-axis increase, values on the y-axis decrease.
    • Example: Temperature vs. Soup sales; higher temperatures lead to decreased soup sales.

Other Associations

  • Patterns may vary depending on different factors like time of day.

Linear vs. Nonlinear Associations

Linear Association

  • Definition: A pattern that can be represented by a straight line.

Nonlinear Association

  • Definition: A pattern best represented by a curve or another shape.
    • Example: Exponential growth seen in COVID-19 graphs.

Strength of Associations

Strong Association

  • Data points are close together, showing a clear pattern.

Moderate Association

  • Data points are more spread out but still show a perceptible pattern.

Weak Association

  • Data points are scattered with less obvious pattern direction.

Outliers in Scatter Plots

  • Definition: Data points that deviate significantly from the overall pattern.
  • Example: Students who do much homework but have low maths marks, or vice versa.

Practical Interpretation

  • Use mathematical terminology (e.g., moderate positive association, outliers) to describe and interpret scatter plots.
  • Interpretation includes assessing the relationship, determining strength, and identifying outliers.

Conclusion

  • With understanding of these concepts, one can look at a scatter plot and interpret the relationships, including identifying any outliers.